In [12]:
import matplotlib.pyplot as plt
plt.figure(figsize=(10,6))
df.status.hist()
plt.xlabel('status')
plt.ylabel('Frequencies')
plt.plot()
plt.show()
import numpy as np
import pandas as pd
import os
os.chdir("C:\\Users\Morvi panchal\OneDrive\Desktop\Practice data science")
df = pd.read_csv("parkinsons.csv")
df.head()
| name | MDVP:Fo(Hz) | MDVP:Fhi(Hz) | MDVP:Flo(Hz) | MDVP:Jitter(%) | MDVP:Jitter(Abs) | MDVP:RAP | MDVP:PPQ | Jitter:DDP | MDVP:Shimmer | ... | Shimmer:DDA | NHR | HNR | status | RPDE | DFA | spread1 | spread2 | D2 | PPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | phon_R01_S01_1 | 119.992 | 157.302 | 74.997 | 0.00784 | 0.00007 | 0.00370 | 0.00554 | 0.01109 | 0.04374 | ... | 0.06545 | 0.02211 | 21.033 | 1 | 0.414783 | 0.815285 | -4.813031 | 0.266482 | 2.301442 | 0.284654 |
| 1 | phon_R01_S01_2 | 122.400 | 148.650 | 113.819 | 0.00968 | 0.00008 | 0.00465 | 0.00696 | 0.01394 | 0.06134 | ... | 0.09403 | 0.01929 | 19.085 | 1 | 0.458359 | 0.819521 | -4.075192 | 0.335590 | 2.486855 | 0.368674 |
| 2 | phon_R01_S01_3 | 116.682 | 131.111 | 111.555 | 0.01050 | 0.00009 | 0.00544 | 0.00781 | 0.01633 | 0.05233 | ... | 0.08270 | 0.01309 | 20.651 | 1 | 0.429895 | 0.825288 | -4.443179 | 0.311173 | 2.342259 | 0.332634 |
| 3 | phon_R01_S01_4 | 116.676 | 137.871 | 111.366 | 0.00997 | 0.00009 | 0.00502 | 0.00698 | 0.01505 | 0.05492 | ... | 0.08771 | 0.01353 | 20.644 | 1 | 0.434969 | 0.819235 | -4.117501 | 0.334147 | 2.405554 | 0.368975 |
| 4 | phon_R01_S01_5 | 116.014 | 141.781 | 110.655 | 0.01284 | 0.00011 | 0.00655 | 0.00908 | 0.01966 | 0.06425 | ... | 0.10470 | 0.01767 | 19.649 | 1 | 0.417356 | 0.823484 | -3.747787 | 0.234513 | 2.332180 | 0.410335 |
5 rows × 24 columns
!pip install pandas-profiling
Requirement already satisfied: pandas-profiling in m:\anaconda\lib\site-packages (3.6.6) Requirement already satisfied: ydata-profiling in m:\anaconda\lib\site-packages (from pandas-profiling) (4.5.1) Requirement already satisfied: PyYAML<6.1,>=5.0.0 in m:\anaconda\lib\site-packages (from ydata-profiling->pandas-profiling) (5.3.1) Requirement already satisfied: jinja2<3.2,>=2.11.1 in m:\anaconda\lib\site-packages (from ydata-profiling->pandas-profiling) (2.11.2) Requirement already satisfied: numpy<1.24,>=1.16.0 in m:\anaconda\lib\site-packages (from ydata-profiling->pandas-profiling) (1.19.2) Requirement already satisfied: scipy<1.12,>=1.4.1 in m:\anaconda\lib\site-packages (from ydata-profiling->pandas-profiling) (1.5.2) Requirement already satisfied: seaborn<0.13,>=0.10.1 in m:\anaconda\lib\site-packages (from ydata-profiling->pandas-profiling) (0.11.0) Requirement already satisfied: htmlmin==0.1.12 in m:\anaconda\lib\site-packages (from ydata-profiling->pandas-profiling) (0.1.12) Requirement already satisfied: visions[type_image_path]==0.7.5 in m:\anaconda\lib\site-packages (from ydata-profiling->pandas-profiling) (0.7.5) Requirement already satisfied: statsmodels<1,>=0.13.2 in m:\anaconda\lib\site-packages (from ydata-profiling->pandas-profiling) (0.14.0) Requirement already satisfied: dacite>=1.8 in m:\anaconda\lib\site-packages (from ydata-profiling->pandas-profiling) (1.8.1) Requirement already satisfied: typeguard<3,>=2.13.2 in m:\anaconda\lib\site-packages (from ydata-profiling->pandas-profiling) (2.13.3) Requirement already satisfied: multimethod<2,>=1.4 in m:\anaconda\lib\site-packages (from ydata-profiling->pandas-profiling) (1.9.1) Requirement already satisfied: imagehash==4.3.1 in m:\anaconda\lib\site-packages (from ydata-profiling->pandas-profiling) (4.3.1) Requirement already satisfied: requests<3,>=2.24.0 in m:\anaconda\lib\site-packages (from ydata-profiling->pandas-profiling) (2.24.0) Requirement already satisfied: tqdm<5,>=4.48.2 in m:\anaconda\lib\site-packages (from ydata-profiling->pandas-profiling) (4.50.2) Requirement already satisfied: wordcloud>=1.9.1 in m:\anaconda\lib\site-packages (from ydata-profiling->pandas-profiling) (1.9.2) Requirement already satisfied: matplotlib<4,>=3.2 in m:\anaconda\lib\site-packages (from ydata-profiling->pandas-profiling) (3.3.2) Requirement already satisfied: pydantic<2,>=1.8.1 in m:\anaconda\lib\site-packages (from ydata-profiling->pandas-profiling) (1.10.12) Requirement already satisfied: pandas!=1.4.0,<2.1,>1.1 in m:\anaconda\lib\site-packages (from ydata-profiling->pandas-profiling) (1.1.3) Requirement already satisfied: phik<0.13,>=0.11.1 in m:\anaconda\lib\site-packages (from ydata-profiling->pandas-profiling) (0.12.3) Requirement already satisfied: MarkupSafe>=0.23 in m:\anaconda\lib\site-packages (from jinja2<3.2,>=2.11.1->ydata-profiling->pandas-profiling) (1.1.1) Requirement already satisfied: networkx>=2.4 in m:\anaconda\lib\site-packages (from visions[type_image_path]==0.7.5->ydata-profiling->pandas-profiling) (2.5) Requirement already satisfied: tangled-up-in-unicode>=0.0.4 in m:\anaconda\lib\site-packages (from visions[type_image_path]==0.7.5->ydata-profiling->pandas-profiling) (0.2.0) Requirement already satisfied: attrs>=19.3.0 in m:\anaconda\lib\site-packages (from visions[type_image_path]==0.7.5->ydata-profiling->pandas-profiling) (20.3.0) Requirement already satisfied: Pillow; extra == "type_image_path" in m:\anaconda\lib\site-packages (from visions[type_image_path]==0.7.5->ydata-profiling->pandas-profiling) (8.0.1) Requirement already satisfied: packaging>=21.3 in m:\anaconda\lib\site-packages (from statsmodels<1,>=0.13.2->ydata-profiling->pandas-profiling) (23.1) Requirement already satisfied: patsy>=0.5.2 in m:\anaconda\lib\site-packages (from statsmodels<1,>=0.13.2->ydata-profiling->pandas-profiling) (0.5.3) Requirement already satisfied: PyWavelets in m:\anaconda\lib\site-packages (from imagehash==4.3.1->ydata-profiling->pandas-profiling) (1.1.1) Requirement already satisfied: certifi>=2017.4.17 in m:\anaconda\lib\site-packages (from requests<3,>=2.24.0->ydata-profiling->pandas-profiling) (2020.6.20) Requirement already satisfied: chardet<4,>=3.0.2 in m:\anaconda\lib\site-packages (from requests<3,>=2.24.0->ydata-profiling->pandas-profiling) (3.0.4) Requirement already satisfied: idna<3,>=2.5 in m:\anaconda\lib\site-packages (from requests<3,>=2.24.0->ydata-profiling->pandas-profiling) (2.10) Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in m:\anaconda\lib\site-packages (from requests<3,>=2.24.0->ydata-profiling->pandas-profiling) (1.25.11) Requirement already satisfied: kiwisolver>=1.0.1 in m:\anaconda\lib\site-packages (from matplotlib<4,>=3.2->ydata-profiling->pandas-profiling) (1.3.0) Requirement already satisfied: python-dateutil>=2.1 in m:\anaconda\lib\site-packages (from matplotlib<4,>=3.2->ydata-profiling->pandas-profiling) (2.8.1) Requirement already satisfied: cycler>=0.10 in m:\anaconda\lib\site-packages (from matplotlib<4,>=3.2->ydata-profiling->pandas-profiling) (0.10.0) Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in m:\anaconda\lib\site-packages (from matplotlib<4,>=3.2->ydata-profiling->pandas-profiling) (2.4.7) Requirement already satisfied: typing-extensions>=4.2.0 in m:\anaconda\lib\site-packages (from pydantic<2,>=1.8.1->ydata-profiling->pandas-profiling) (4.8.0) Requirement already satisfied: pytz>=2017.2 in m:\anaconda\lib\site-packages (from pandas!=1.4.0,<2.1,>1.1->ydata-profiling->pandas-profiling) (2020.1) Requirement already satisfied: joblib>=0.14.1 in m:\anaconda\lib\site-packages (from phik<0.13,>=0.11.1->ydata-profiling->pandas-profiling) (0.17.0) Requirement already satisfied: decorator>=4.3.0 in m:\anaconda\lib\site-packages (from networkx>=2.4->visions[type_image_path]==0.7.5->ydata-profiling->pandas-profiling) (4.4.2) Requirement already satisfied: six in m:\anaconda\lib\site-packages (from patsy>=0.5.2->statsmodels<1,>=0.13.2->ydata-profiling->pandas-profiling) (1.15.0)
import pandas_profiling as pf
display(pf.ProfileReport(df))
<ipython-input-6-7c263133eb4e>:1: DeprecationWarning: `import pandas_profiling` is going to be deprecated by April 1st. Please use `import ydata_profiling` instead. import pandas_profiling as pf
display(df.shape)
(195, 24)
display(len(df))
195
print(df.info)
<bound method DataFrame.info of name MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz) MDVP:Jitter(%) \
0 phon_R01_S01_1 119.992 157.302 74.997 0.00784
1 phon_R01_S01_2 122.400 148.650 113.819 0.00968
2 phon_R01_S01_3 116.682 131.111 111.555 0.01050
3 phon_R01_S01_4 116.676 137.871 111.366 0.00997
4 phon_R01_S01_5 116.014 141.781 110.655 0.01284
.. ... ... ... ... ...
190 phon_R01_S50_2 174.188 230.978 94.261 0.00459
191 phon_R01_S50_3 209.516 253.017 89.488 0.00564
192 phon_R01_S50_4 174.688 240.005 74.287 0.01360
193 phon_R01_S50_5 198.764 396.961 74.904 0.00740
194 phon_R01_S50_6 214.289 260.277 77.973 0.00567
MDVP:Jitter(Abs) MDVP:RAP MDVP:PPQ Jitter:DDP MDVP:Shimmer ... \
0 0.00007 0.00370 0.00554 0.01109 0.04374 ...
1 0.00008 0.00465 0.00696 0.01394 0.06134 ...
2 0.00009 0.00544 0.00781 0.01633 0.05233 ...
3 0.00009 0.00502 0.00698 0.01505 0.05492 ...
4 0.00011 0.00655 0.00908 0.01966 0.06425 ...
.. ... ... ... ... ... ...
190 0.00003 0.00263 0.00259 0.00790 0.04087 ...
191 0.00003 0.00331 0.00292 0.00994 0.02751 ...
192 0.00008 0.00624 0.00564 0.01873 0.02308 ...
193 0.00004 0.00370 0.00390 0.01109 0.02296 ...
194 0.00003 0.00295 0.00317 0.00885 0.01884 ...
Shimmer:DDA NHR HNR status RPDE DFA spread1 \
0 0.06545 0.02211 21.033 1 0.414783 0.815285 -4.813031
1 0.09403 0.01929 19.085 1 0.458359 0.819521 -4.075192
2 0.08270 0.01309 20.651 1 0.429895 0.825288 -4.443179
3 0.08771 0.01353 20.644 1 0.434969 0.819235 -4.117501
4 0.10470 0.01767 19.649 1 0.417356 0.823484 -3.747787
.. ... ... ... ... ... ... ...
190 0.07008 0.02764 19.517 0 0.448439 0.657899 -6.538586
191 0.04812 0.01810 19.147 0 0.431674 0.683244 -6.195325
192 0.03804 0.10715 17.883 0 0.407567 0.655683 -6.787197
193 0.03794 0.07223 19.020 0 0.451221 0.643956 -6.744577
194 0.03078 0.04398 21.209 0 0.462803 0.664357 -5.724056
spread2 D2 PPE
0 0.266482 2.301442 0.284654
1 0.335590 2.486855 0.368674
2 0.311173 2.342259 0.332634
3 0.334147 2.405554 0.368975
4 0.234513 2.332180 0.410335
.. ... ... ...
190 0.121952 2.657476 0.133050
191 0.129303 2.784312 0.168895
192 0.158453 2.679772 0.131728
193 0.207454 2.138608 0.123306
194 0.190667 2.555477 0.148569
[195 rows x 24 columns]>
display(df.isna().sum())
name 0 MDVP:Fo(Hz) 0 MDVP:Fhi(Hz) 0 MDVP:Flo(Hz) 0 MDVP:Jitter(%) 0 MDVP:Jitter(Abs) 0 MDVP:RAP 0 MDVP:PPQ 0 Jitter:DDP 0 MDVP:Shimmer 0 MDVP:Shimmer(dB) 0 Shimmer:APQ3 0 Shimmer:APQ5 0 MDVP:APQ 0 Shimmer:DDA 0 NHR 0 HNR 0 status 0 RPDE 0 DFA 0 spread1 0 spread2 0 D2 0 PPE 0 dtype: int64
print(df.status)
0 1
1 1
2 1
3 1
4 1
..
190 0
191 0
192 0
193 0
194 0
Name: status, Length: 195, dtype: int64
import matplotlib.pyplot as plt
plt.figure(figsize=(10,6))
df.status.hist()
plt.xlabel('status')
plt.ylabel('Frequencies')
plt.plot()
plt.show()
import seaborn as sns
plt.figure(figsize=(10,6))
sns.barplot(x='status',y="HNR",data=df)
plt.show()
plt.figure(figsize=(10,6))
sns.barplot(x='status',y='RPDE',data=df);
plt.show()
import warnings
warnings.filterwarnings('ignore')
rows=3
cols=7
fig, ax=plt.subplots(nrows=rows,ncols=cols,figsize=(16,4))
col=df.columns
index=1
for i in range(rows):
for j in range(cols):
sns.distplot(df[col[index]],ax=ax[i][j])
index=index+1
plt.tight_layout()
plt.show()
corr = df.corr()
display(corr)
| MDVP:Fo(Hz) | MDVP:Fhi(Hz) | MDVP:Flo(Hz) | MDVP:Jitter(%) | MDVP:Jitter(Abs) | MDVP:RAP | MDVP:PPQ | Jitter:DDP | MDVP:Shimmer | MDVP:Shimmer(dB) | ... | Shimmer:DDA | NHR | HNR | status | RPDE | DFA | spread1 | spread2 | D2 | PPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MDVP:Fo(Hz) | 1.000000 | 0.400985 | 0.596546 | -0.118003 | -0.382027 | -0.076194 | -0.112165 | -0.076213 | -0.098374 | -0.073742 | ... | -0.094732 | -0.021981 | 0.059144 | -0.383535 | -0.383894 | -0.446013 | -0.413738 | -0.249450 | 0.177980 | -0.372356 |
| MDVP:Fhi(Hz) | 0.400985 | 1.000000 | 0.084951 | 0.102086 | -0.029198 | 0.097177 | 0.091126 | 0.097150 | 0.002281 | 0.043465 | ... | -0.003733 | 0.163766 | -0.024893 | -0.166136 | -0.112404 | -0.343097 | -0.076658 | -0.002954 | 0.176323 | -0.069543 |
| MDVP:Flo(Hz) | 0.596546 | 0.084951 | 1.000000 | -0.139919 | -0.277815 | -0.100519 | -0.095828 | -0.100488 | -0.144543 | -0.119089 | ... | -0.150737 | -0.108670 | 0.210851 | -0.380200 | -0.400143 | -0.050406 | -0.394857 | -0.243829 | -0.100629 | -0.340071 |
| MDVP:Jitter(%) | -0.118003 | 0.102086 | -0.139919 | 1.000000 | 0.935714 | 0.990276 | 0.974256 | 0.990276 | 0.769063 | 0.804289 | ... | 0.746635 | 0.906959 | -0.728165 | 0.278220 | 0.360673 | 0.098572 | 0.693577 | 0.385123 | 0.433434 | 0.721543 |
| MDVP:Jitter(Abs) | -0.382027 | -0.029198 | -0.277815 | 0.935714 | 1.000000 | 0.922911 | 0.897778 | 0.922913 | 0.703322 | 0.716601 | ... | 0.697170 | 0.834972 | -0.656810 | 0.338653 | 0.441839 | 0.175036 | 0.735779 | 0.388543 | 0.310694 | 0.748162 |
| MDVP:RAP | -0.076194 | 0.097177 | -0.100519 | 0.990276 | 0.922911 | 1.000000 | 0.957317 | 1.000000 | 0.759581 | 0.790652 | ... | 0.744919 | 0.919521 | -0.721543 | 0.266668 | 0.342140 | 0.064083 | 0.648328 | 0.324407 | 0.426605 | 0.670999 |
| MDVP:PPQ | -0.112165 | 0.091126 | -0.095828 | 0.974256 | 0.897778 | 0.957317 | 1.000000 | 0.957319 | 0.797826 | 0.839239 | ... | 0.763592 | 0.844604 | -0.731510 | 0.288698 | 0.333274 | 0.196301 | 0.716489 | 0.407605 | 0.412524 | 0.769647 |
| Jitter:DDP | -0.076213 | 0.097150 | -0.100488 | 0.990276 | 0.922913 | 1.000000 | 0.957319 | 1.000000 | 0.759555 | 0.790621 | ... | 0.744901 | 0.919548 | -0.721494 | 0.266646 | 0.342079 | 0.064026 | 0.648328 | 0.324377 | 0.426556 | 0.671005 |
| MDVP:Shimmer | -0.098374 | 0.002281 | -0.144543 | 0.769063 | 0.703322 | 0.759581 | 0.797826 | 0.759555 | 1.000000 | 0.987258 | ... | 0.987626 | 0.722194 | -0.835271 | 0.367430 | 0.447424 | 0.159954 | 0.654734 | 0.452025 | 0.507088 | 0.693771 |
| MDVP:Shimmer(dB) | -0.073742 | 0.043465 | -0.119089 | 0.804289 | 0.716601 | 0.790652 | 0.839239 | 0.790621 | 0.987258 | 1.000000 | ... | 0.963202 | 0.744477 | -0.827805 | 0.350697 | 0.410684 | 0.165157 | 0.652547 | 0.454314 | 0.512233 | 0.695058 |
| Shimmer:APQ3 | -0.094717 | -0.003743 | -0.150747 | 0.746625 | 0.697153 | 0.744912 | 0.763580 | 0.744894 | 0.987625 | 0.963198 | ... | 1.000000 | 0.716207 | -0.827123 | 0.347617 | 0.435242 | 0.151124 | 0.610967 | 0.402243 | 0.467265 | 0.645377 |
| Shimmer:APQ5 | -0.070682 | -0.009997 | -0.101095 | 0.725561 | 0.648961 | 0.709927 | 0.786780 | 0.709907 | 0.982835 | 0.973751 | ... | 0.960072 | 0.658080 | -0.813753 | 0.351148 | 0.399903 | 0.213873 | 0.646809 | 0.457195 | 0.502174 | 0.702456 |
| MDVP:APQ | -0.077774 | 0.004937 | -0.107293 | 0.758255 | 0.648793 | 0.737455 | 0.804139 | 0.737439 | 0.950083 | 0.960977 | ... | 0.896647 | 0.694019 | -0.800407 | 0.364316 | 0.451379 | 0.157276 | 0.673158 | 0.502188 | 0.536869 | 0.721694 |
| Shimmer:DDA | -0.094732 | -0.003733 | -0.150737 | 0.746635 | 0.697170 | 0.744919 | 0.763592 | 0.744901 | 0.987626 | 0.963202 | ... | 1.000000 | 0.716215 | -0.827130 | 0.347608 | 0.435237 | 0.151132 | 0.610971 | 0.402223 | 0.467261 | 0.645389 |
| NHR | -0.021981 | 0.163766 | -0.108670 | 0.906959 | 0.834972 | 0.919521 | 0.844604 | 0.919548 | 0.722194 | 0.744477 | ... | 0.716215 | 1.000000 | -0.714072 | 0.189429 | 0.370890 | -0.131882 | 0.540865 | 0.318099 | 0.470949 | 0.552591 |
| HNR | 0.059144 | -0.024893 | 0.210851 | -0.728165 | -0.656810 | -0.721543 | -0.731510 | -0.721494 | -0.835271 | -0.827805 | ... | -0.827130 | -0.714072 | 1.000000 | -0.361515 | -0.598736 | -0.008665 | -0.673210 | -0.431564 | -0.601401 | -0.692876 |
| status | -0.383535 | -0.166136 | -0.380200 | 0.278220 | 0.338653 | 0.266668 | 0.288698 | 0.266646 | 0.367430 | 0.350697 | ... | 0.347608 | 0.189429 | -0.361515 | 1.000000 | 0.308567 | 0.231739 | 0.564838 | 0.454842 | 0.340232 | 0.531039 |
| RPDE | -0.383894 | -0.112404 | -0.400143 | 0.360673 | 0.441839 | 0.342140 | 0.333274 | 0.342079 | 0.447424 | 0.410684 | ... | 0.435237 | 0.370890 | -0.598736 | 0.308567 | 1.000000 | -0.110950 | 0.591117 | 0.479905 | 0.236931 | 0.545886 |
| DFA | -0.446013 | -0.343097 | -0.050406 | 0.098572 | 0.175036 | 0.064083 | 0.196301 | 0.064026 | 0.159954 | 0.165157 | ... | 0.151132 | -0.131882 | -0.008665 | 0.231739 | -0.110950 | 1.000000 | 0.195668 | 0.166548 | -0.165381 | 0.270445 |
| spread1 | -0.413738 | -0.076658 | -0.394857 | 0.693577 | 0.735779 | 0.648328 | 0.716489 | 0.648328 | 0.654734 | 0.652547 | ... | 0.610971 | 0.540865 | -0.673210 | 0.564838 | 0.591117 | 0.195668 | 1.000000 | 0.652358 | 0.495123 | 0.962435 |
| spread2 | -0.249450 | -0.002954 | -0.243829 | 0.385123 | 0.388543 | 0.324407 | 0.407605 | 0.324377 | 0.452025 | 0.454314 | ... | 0.402223 | 0.318099 | -0.431564 | 0.454842 | 0.479905 | 0.166548 | 0.652358 | 1.000000 | 0.523532 | 0.644711 |
| D2 | 0.177980 | 0.176323 | -0.100629 | 0.433434 | 0.310694 | 0.426605 | 0.412524 | 0.426556 | 0.507088 | 0.512233 | ... | 0.467261 | 0.470949 | -0.601401 | 0.340232 | 0.236931 | -0.165381 | 0.495123 | 0.523532 | 1.000000 | 0.480585 |
| PPE | -0.372356 | -0.069543 | -0.340071 | 0.721543 | 0.748162 | 0.670999 | 0.769647 | 0.671005 | 0.693771 | 0.695058 | ... | 0.645389 | 0.552591 | -0.692876 | 0.531039 | 0.545886 | 0.270445 | 0.962435 | 0.644711 | 0.480585 | 1.000000 |
23 rows × 23 columns
df.drop(['name'],axis=1,inplace=True)
display (df)
| MDVP:Fo(Hz) | MDVP:Fhi(Hz) | MDVP:Flo(Hz) | MDVP:Jitter(%) | MDVP:Jitter(Abs) | MDVP:RAP | MDVP:PPQ | Jitter:DDP | MDVP:Shimmer | MDVP:Shimmer(dB) | ... | Shimmer:DDA | NHR | HNR | status | RPDE | DFA | spread1 | spread2 | D2 | PPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 119.992 | 157.302 | 74.997 | 0.00784 | 0.00007 | 0.00370 | 0.00554 | 0.01109 | 0.04374 | 0.426 | ... | 0.06545 | 0.02211 | 21.033 | 1 | 0.414783 | 0.815285 | -4.813031 | 0.266482 | 2.301442 | 0.284654 |
| 1 | 122.400 | 148.650 | 113.819 | 0.00968 | 0.00008 | 0.00465 | 0.00696 | 0.01394 | 0.06134 | 0.626 | ... | 0.09403 | 0.01929 | 19.085 | 1 | 0.458359 | 0.819521 | -4.075192 | 0.335590 | 2.486855 | 0.368674 |
| 2 | 116.682 | 131.111 | 111.555 | 0.01050 | 0.00009 | 0.00544 | 0.00781 | 0.01633 | 0.05233 | 0.482 | ... | 0.08270 | 0.01309 | 20.651 | 1 | 0.429895 | 0.825288 | -4.443179 | 0.311173 | 2.342259 | 0.332634 |
| 3 | 116.676 | 137.871 | 111.366 | 0.00997 | 0.00009 | 0.00502 | 0.00698 | 0.01505 | 0.05492 | 0.517 | ... | 0.08771 | 0.01353 | 20.644 | 1 | 0.434969 | 0.819235 | -4.117501 | 0.334147 | 2.405554 | 0.368975 |
| 4 | 116.014 | 141.781 | 110.655 | 0.01284 | 0.00011 | 0.00655 | 0.00908 | 0.01966 | 0.06425 | 0.584 | ... | 0.10470 | 0.01767 | 19.649 | 1 | 0.417356 | 0.823484 | -3.747787 | 0.234513 | 2.332180 | 0.410335 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 190 | 174.188 | 230.978 | 94.261 | 0.00459 | 0.00003 | 0.00263 | 0.00259 | 0.00790 | 0.04087 | 0.405 | ... | 0.07008 | 0.02764 | 19.517 | 0 | 0.448439 | 0.657899 | -6.538586 | 0.121952 | 2.657476 | 0.133050 |
| 191 | 209.516 | 253.017 | 89.488 | 0.00564 | 0.00003 | 0.00331 | 0.00292 | 0.00994 | 0.02751 | 0.263 | ... | 0.04812 | 0.01810 | 19.147 | 0 | 0.431674 | 0.683244 | -6.195325 | 0.129303 | 2.784312 | 0.168895 |
| 192 | 174.688 | 240.005 | 74.287 | 0.01360 | 0.00008 | 0.00624 | 0.00564 | 0.01873 | 0.02308 | 0.256 | ... | 0.03804 | 0.10715 | 17.883 | 0 | 0.407567 | 0.655683 | -6.787197 | 0.158453 | 2.679772 | 0.131728 |
| 193 | 198.764 | 396.961 | 74.904 | 0.00740 | 0.00004 | 0.00370 | 0.00390 | 0.01109 | 0.02296 | 0.241 | ... | 0.03794 | 0.07223 | 19.020 | 0 | 0.451221 | 0.643956 | -6.744577 | 0.207454 | 2.138608 | 0.123306 |
| 194 | 214.289 | 260.277 | 77.973 | 0.00567 | 0.00003 | 0.00295 | 0.00317 | 0.00885 | 0.01884 | 0.190 | ... | 0.03078 | 0.04398 | 21.209 | 0 | 0.462803 | 0.664357 | -5.724056 | 0.190667 | 2.555477 | 0.148569 |
195 rows × 23 columns
x = df.drop('status',axis=1)
display(x.head())
| MDVP:Fo(Hz) | MDVP:Fhi(Hz) | MDVP:Flo(Hz) | MDVP:Jitter(%) | MDVP:Jitter(Abs) | MDVP:RAP | MDVP:PPQ | Jitter:DDP | MDVP:Shimmer | MDVP:Shimmer(dB) | ... | MDVP:APQ | Shimmer:DDA | NHR | HNR | RPDE | DFA | spread1 | spread2 | D2 | PPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 119.992 | 157.302 | 74.997 | 0.00784 | 0.00007 | 0.00370 | 0.00554 | 0.01109 | 0.04374 | 0.426 | ... | 0.02971 | 0.06545 | 0.02211 | 21.033 | 0.414783 | 0.815285 | -4.813031 | 0.266482 | 2.301442 | 0.284654 |
| 1 | 122.400 | 148.650 | 113.819 | 0.00968 | 0.00008 | 0.00465 | 0.00696 | 0.01394 | 0.06134 | 0.626 | ... | 0.04368 | 0.09403 | 0.01929 | 19.085 | 0.458359 | 0.819521 | -4.075192 | 0.335590 | 2.486855 | 0.368674 |
| 2 | 116.682 | 131.111 | 111.555 | 0.01050 | 0.00009 | 0.00544 | 0.00781 | 0.01633 | 0.05233 | 0.482 | ... | 0.03590 | 0.08270 | 0.01309 | 20.651 | 0.429895 | 0.825288 | -4.443179 | 0.311173 | 2.342259 | 0.332634 |
| 3 | 116.676 | 137.871 | 111.366 | 0.00997 | 0.00009 | 0.00502 | 0.00698 | 0.01505 | 0.05492 | 0.517 | ... | 0.03772 | 0.08771 | 0.01353 | 20.644 | 0.434969 | 0.819235 | -4.117501 | 0.334147 | 2.405554 | 0.368975 |
| 4 | 116.014 | 141.781 | 110.655 | 0.01284 | 0.00011 | 0.00655 | 0.00908 | 0.01966 | 0.06425 | 0.584 | ... | 0.04465 | 0.10470 | 0.01767 | 19.649 | 0.417356 | 0.823484 | -3.747787 | 0.234513 | 2.332180 | 0.410335 |
5 rows × 22 columns
y = df.status
display(y.head())
0 1 1 1 2 1 3 1 4 1 Name: status, dtype: int64
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=40)
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
lg= LogisticRegression().fit(x_train, y_train)
train_preds = lg.predict(x_train)
print("Model accuracy on train is: ", accuracy_score(y_train, train_preds))
test_preds = lg.predict(x_test)
print("Model accuracy on test is: ", accuracy_score(y_test, test_preds))
print('-'*50)
Model accuracy on train is: 0.8782051282051282 Model accuracy on test is: 0.8461538461538461 --------------------------------------------------
print("confusion_matrix train is:\n ", confusion_matrix(y_train, train_preds))
print("confusion_matrix test is:\n ", confusion_matrix(y_test, test_preds))
print('\nClassification Report Train is ')
print(classification_report (y_train, train_preds))
print('\nClassification Report Test is ')
print(classification_report (y_test, test_preds))
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
rf=RandomForestClassifier().fit(x_train,y_train)
trainpreds2 = rf.predict(x_train)
print("Model accuracy on train is: ", accuracy_score(y_train, trainpreds2))
testpreds2 = rf.predict(x_test)
print("Model accuracy on test is: ", accuracy_score(y_test, testpreds2))
Model accuracy on train is: 1.0 Model accuracy on test is: 0.9230769230769231
print("Confusion matrix of train is: ",confusion_matrix(y_train,trainpreds2))
print("Confusion matrix of test is: ",confusion_matrix(y_test,testpreds2))
print("Classification matrix of train is: ",classification_report(y_train,trainpreds2))
print("Classification matrix of test is: ",classification_report(y_test,testpreds2))
Confusion matrix of train is: [[ 40 0]
[ 0 116]]
Confusion matrix of test is: [[ 6 2]
[ 1 30]]
Classification matrix of train is: precision recall f1-score support
0 1.00 1.00 1.00 40
1 1.00 1.00 1.00 116
accuracy 1.00 156
macro avg 1.00 1.00 1.00 156
weighted avg 1.00 1.00 1.00 156
Classification matrix of test is: precision recall f1-score support
0 0.86 0.75 0.80 8
1 0.94 0.97 0.95 31
accuracy 0.92 39
macro avg 0.90 0.86 0.88 39
weighted avg 0.92 0.92 0.92 39
print((y_test !=testpreds2).sum(),'/',((y_test == testpreds2).sum()+(y_test != testpreds2).sum()))
3 / 39
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
import os
print('The kappa sore is: ',metrics.cohen_kappa_score(y_test,testpreds2))
The kappa sore is: 0.7526427061310782
ddf = pd.DataFrame(data=[testpreds2,y_test])
display(ddf)
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 |
| 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | ... | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 |
2 rows × 39 columns
display(ddf.T)
| 0 | 1 | |
|---|---|---|
| 0 | 1 | 1 |
| 1 | 1 | 1 |
| 2 | 1 | 1 |
| 3 | 0 | 0 |
| 4 | 1 | 1 |
| 5 | 1 | 0 |
| 6 | 1 | 1 |
| 7 | 1 | 1 |
| 8 | 1 | 1 |
| 9 | 1 | 0 |
| 10 | 1 | 1 |
| 11 | 1 | 1 |
| 12 | 0 | 0 |
| 13 | 1 | 1 |
| 14 | 1 | 1 |
| 15 | 1 | 1 |
| 16 | 1 | 1 |
| 17 | 1 | 1 |
| 18 | 1 | 1 |
| 19 | 1 | 1 |
| 20 | 1 | 1 |
| 21 | 0 | 1 |
| 22 | 1 | 1 |
| 23 | 1 | 1 |
| 24 | 0 | 0 |
| 25 | 1 | 1 |
| 26 | 1 | 1 |
| 27 | 1 | 1 |
| 28 | 1 | 1 |
| 29 | 1 | 1 |
| 30 | 0 | 0 |
| 31 | 0 | 0 |
| 32 | 0 | 0 |
| 33 | 1 | 1 |
| 34 | 1 | 1 |
| 35 | 1 | 1 |
| 36 | 1 | 1 |
| 37 | 1 | 1 |
| 38 | 1 | 1 |
from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier().fit(x,y)
trainpred3 = dt.predict(x_train)
print("The accuracy of decision tree(train) is: ",accuracy_score(y_train,trainpred3))
testpreds3 = dt.predict(x_test)
print("The accuracy of decision trr(test) is: ",accuracy_score(y_test,testpreds3))
The accuracy of decision tree(train) is: 1.0 The accuracy of decision trr(test) is: 1.0
print("The train confusion matric is: ",confusion_matrix(y_train,trainpred3))
print("The test confusion matric is: ",confusion_matrix(y_test,testpreds3))
The train confusion matric is: [[ 40 0] [ 0 116]] The test confusion matric is: [[ 8 0] [ 0 31]]
print("The classification report is for train: ",classification_report(y_train,trainpred3))
print("The classification report is for test: ",classification_report(y_test,testpreds3))
The classification report is for train: precision recall f1-score support
0 1.00 1.00 1.00 40
1 1.00 1.00 1.00 116
accuracy 1.00 156
macro avg 1.00 1.00 1.00 156
weighted avg 1.00 1.00 1.00 156
The classification report is for test: precision recall f1-score support
0 1.00 1.00 1.00 8
1 1.00 1.00 1.00 31
accuracy 1.00 39
macro avg 1.00 1.00 1.00 39
weighted avg 1.00 1.00 1.00 39
print((y_test !=testpreds3).sum(),'/',((y_test == testpreds3).sum()+(y_test != testpreds3).sum()))
0 / 39
print("Kappa score is: ",metrics.cohen_kappa_score(y_test,testpreds3))
Kappa score is: 1.0
from sklearn.naive_bayes import GaussianNB
#fit the model on train data
nb=GaussianNB()
nb.fit(x_train,y_train)
trainpreds4 = nb.predict(x_train)
print("Model accuracy on train is: ", accuracy_score(y_train, trainpreds4))
testpreds4 = nb.predict(x_test)
print("Model accuracy on test is: ", accuracy_score(y_test, testpreds4))
Model accuracy on train is: 0.7307692307692307 Model accuracy on test is: 0.6923076923076923
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
print("Classification matrix of train is : ",confusion_matrix(y_train,trainpreds4))
print("Classification matrix of test is: ",confusion_matrix(y_test,testpreds4))
Classification matrix of train is : [[38 2] [40 76]] Classification matrix of test is: [[ 8 0] [12 19]]
print("Classification matrix of train is: ",classification_report(y_train,trainpreds4))
print("Classification matrix of test is: ",classification_report(y_test,testpreds4))
Classification matrix of train is: precision recall f1-score support
0 0.49 0.95 0.64 40
1 0.97 0.66 0.78 116
accuracy 0.73 156
macro avg 0.73 0.80 0.71 156
weighted avg 0.85 0.73 0.75 156
Classification matrix of test is: precision recall f1-score support
0 0.40 1.00 0.57 8
1 1.00 0.61 0.76 31
accuracy 0.69 39
macro avg 0.70 0.81 0.67 39
weighted avg 0.88 0.69 0.72 39
print((y_test!=testpreds4).sum(),'/',((y_test==testpreds4).sum()+(y_test!=testpreds4).sum()))
12 / 39
from sklearn.neighbors import KNeighborsClassifier
kn = KNeighborsClassifier().fit(x_train,y_train)
trainpreds5 = kn.predict(x_train)
print("The accuracy train is: ",accuracy_score(y_train,trainpreds5))
testpreds5 = kn.predict(x_test)
print("The accuracy test is: ",accuracy_score(y_test,testpreds5))
The accuracy train is: 0.9102564102564102 The accuracy test is: 0.8461538461538461
print("Confusion matrix of train is: ",confusion_matrix(y_train,trainpreds5))
print("Confusion matrix of test is: ",confusion_matrix(y_test,testpreds5))
Confusion matrix of train is: [[ 30 10] [ 4 112]] Confusion matrix of test is: [[ 4 4] [ 2 29]]
print("Classification matrix of train is: ",classification_report(y_train,trainpreds5))
print("confusion matrix of test is: ",classification_report(y_test,testpreds5))
Classification matrix of train is: precision recall f1-score support
0 0.88 0.75 0.81 40
1 0.92 0.97 0.94 116
accuracy 0.91 156
macro avg 0.90 0.86 0.88 156
weighted avg 0.91 0.91 0.91 156
confusion matrix of test is: precision recall f1-score support
0 0.67 0.50 0.57 8
1 0.88 0.94 0.91 31
accuracy 0.85 39
macro avg 0.77 0.72 0.74 39
weighted avg 0.84 0.85 0.84 39
print('KappaScore is: ', metrics.cohen_kappa_score(y_test,test_preds5))
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-45-cf615e1a1029> in <module> ----> 1 print('KappaScore is: ', metrics.cohen_kappa_score(y_test,test_preds5)) NameError: name 'test_preds5' is not defined
print('KappaScore is: ', metrics.cohen_kappa_score(y_test,testpreds5))
KappaScore is: 0.48
from sklearn.svm import SVC
sv = SVC()
sv.fit(x_train,y_train)
trainpreds6 =sv.predict(x_train)
print("Accuracy of train is: ",accuracy_score(y_train,trainpreds6))
testpreds6 = sv.predict(x_test)
print("Accuracy of test is: ",accuracy_score(y_test,testpreds6))
Accuracy of train is: 0.8141025641025641 Accuracy of test is: 0.8205128205128205
print("Confusion matrix of train is: ",confusion_matrix(y_train,trainpreds6))
print("Confusion matrix of test is: ",confusion_matrix(y_test,testpreds6))
Confusion matrix of train is: [[ 11 29] [ 0 116]] Confusion matrix of test is: [[ 2 6] [ 1 30]]
print("Classification report of train is: ",classification_report(y_train,trainpreds6))
print("Classification report of train is: ",classification_report(y_test,testpreds6))
Classification report of train is: precision recall f1-score support
0 1.00 0.28 0.43 40
1 0.80 1.00 0.89 116
accuracy 0.81 156
macro avg 0.90 0.64 0.66 156
weighted avg 0.85 0.81 0.77 156
Classification report of train is: precision recall f1-score support
0 0.67 0.25 0.36 8
1 0.83 0.97 0.90 31
accuracy 0.82 39
macro avg 0.75 0.61 0.63 39
weighted avg 0.80 0.82 0.79 39
print('KappaScore is: ', metrics.cohen_kappa_score(y_test,testpreds6))
KappaScore is: 0.2834645669291339
print((y_test !=testpreds6).sum(),'/',((y_test == testpreds6).sum()+(y_test != testpreds6).sum()))
7 / 39
sv.predict([[116.682,131.111,111.555,0.0105,0.00009,0.00544,0.00781,0.01633,0.05233,0.482,0.02757,0.03858,0.0359,0.0827,0.01309,20.651,0.429895,0.825288,-4.443179,0.311173,2.342259,0.332634
]])
array([1], dtype=int64)
import pickle
pickle.dump(dt,open('DecisionTreeParkinsons.pkl','wb'))
model = pickle.load(open('DecisionTreeParkinsons.pkl','rb'))